Despite the recent progress, speaker diarization is still one of the most challenging speech processing tasks [1]}.
Research in this field is very active, and it is fostered by popular challenges such as DIHARD [1]}.
As for clustering, various approaches have been proposed in the literature, including top-down and bottom-up agglomerative clustering [3]}. Spectral clustering, which is a graph clustering method based on the eigenanalysis of the Laplacian matrix, has recently shown promising performance on speaker diarization [4]}, [5]}, [6]}.
